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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12203 |
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| _version_ | 1866916693288157184 |
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| author | Lippenszky, Levente Megyeri, István Koos, Krisztian Karancsi, Zsófia Deák-Karancsi, Borbála Frontó, András Makk, Árpád Rádics, Attila Bas, Erhan Ruskó, László |
| author_facet | Lippenszky, Levente Megyeri, István Koos, Krisztian Karancsi, Zsófia Deák-Karancsi, Borbála Frontó, András Makk, Árpád Rádics, Attila Bas, Erhan Ruskó, László |
| contents | In radiation therapy planning, inaccurate segmentations of organs at risk can result in suboptimal treatment delivery, if left undetected by the clinician. To address this challenge, we developed a denoising autoencoder-based method to detect inaccurate organ segmentations. We applied noise to ground truth organ segmentations, and the autoencoders were tasked to denoise them. Through the application of our method to organ segmentations generated on both MR and CT scans, we demonstrated that the method is independent of imaging modality. By providing reconstructions, our method offers visual information about inaccurate regions of the organ segmentations, leading to more explainable detection of suboptimal segmentations. We compared our method to existing approaches in the literature and demonstrated that it achieved superior performance for the majority of organs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Lippenszky, Levente Megyeri, István Koos, Krisztian Karancsi, Zsófia Deák-Karancsi, Borbála Frontó, András Makk, Árpád Rádics, Attila Bas, Erhan Ruskó, László Image and Video Processing Computer Vision and Pattern Recognition In radiation therapy planning, inaccurate segmentations of organs at risk can result in suboptimal treatment delivery, if left undetected by the clinician. To address this challenge, we developed a denoising autoencoder-based method to detect inaccurate organ segmentations. We applied noise to ground truth organ segmentations, and the autoencoders were tasked to denoise them. Through the application of our method to organ segmentations generated on both MR and CT scans, we demonstrated that the method is independent of imaging modality. By providing reconstructions, our method offers visual information about inaccurate regions of the organ segmentations, leading to more explainable detection of suboptimal segmentations. We compared our method to existing approaches in the literature and demonstrated that it achieved superior performance for the majority of organs. |
| title | Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.12203 |